DSaPP Director Rayid Ghani, Aequitas Featured in Article on Bias and Data-Driven Policy in Philadelphia

Our Data-Driven Police Early Intervention System is live at Charlotte Mecklenburg Police Department

Data. Policy. Impact.

The Center for Data Science and Public Policy (DSaPP) at the University of Chicago is a collaboration between the Computer Science Department and the Harris School of Public Policy to further the use of data science in public policy research and practice. Our work includes educating current and future policymakers, doing data science projects with government, nonprofit, academic, and foundation partners, and developing new methods and open-source tools that support and extend the use of data science for public policy and social impact. Our team is made up of data scientists and researchers from computer science, statistics, and social science backgrounds to bring in methods from all of these disciplines, software engineers to make sure our work becomes usable code and implemented, topic and policy experts to provide context and relevance, and project managers who help get things done.

Our trainings for governments and non profits are designed for Directors and Executives of organizations as well as Analysts and Policymakers.

Data Science Projects

We work with governments, non-profits, and other organizations on data science projects across health, criminal justice, public safety, education, economic development, transportation, and more. Most of our projects tackle operational problems that have tangible impact, and result in software that can be used by our partner organizations (and others) for social impact and improved policies. Recent examples of our projects include:

Building Data-Driven Police Early Intervention Systems

Prioritizing Preventative Lead Hazard Inspections

Prioritizing Health and Safety Housing Inspections

Reducing incarcerations by identifying at risk individuals in need of social services

Research Areas

Our research initiatives are motivated by working on hands-on data science projects with governments, non-profits, and other policy organizations. As we tackle policy problems, we identify open areas where existing methods from computer science, machine learning, artificial intelligence or social sciences are lacking and formulate our research initiatives to fill those gaps. We then push the results of our research back into our data science tools so they can be used across our projects and by our project partners. We are currently working on:

Auditing and Correcting for Bias and Equity Issues in Data Science Systems

Increasing the interpretability and transparency of machine learning models used in policy decisions

Data Science Pipelines and Tools

We believe in open and reusable code and tools. All of our (non-confidential) project code is available under an open source license on our github page. All of our internal data science tools are also available for other organizations to use. Examples of such tools include:

Triage: Our data science pipeline platform that’s used in many of our internal projects, which contains components for generating features, building machine learning models, and evaluating those models.

Entity Deduplication Tool (pgdedupe)

Post-Modeling Tools for analyzing the models built, feature importances, and exploring the outputs of those models before deployment.

Bias Audits: To run bias audits on the outputs of machine learning models